Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.
Motion planning and control are critical operations in autonomous driving of autonomous driving vehicles (ADVs). Conventional motion planning operations estimate the difficulty of completing a given path mainly from its curvature and speed, without considering the differences in features for different types of vehicles. Same motion planning and control is applied to all types of vehicles, which may not be accurate and smooth under some circumstances.
However, for different driving parameters such as speeds or heading directions, controller coefficients of a controller for controlling an ADV may be different. Tuning coefficients or gains of a controller for autonomous driving is painful. There are many coefficients for different controllers and mapping the coefficients and the driving parameters are not linear. Controller coefficients may deviate from optimal points as the vehicle parameters deteriorate. There has been a lack of efficient ways to fine tune the controller coefficients of controllers for autonomous driving.